1. Field of the Disclosure
Embodiments relate to making predictions using spatial patterns and temporal sequences learned by a spatial and temporal memory system, and more specifically to making predictions for values, states or distribution of values to follow multiple time steps after a current time using the spatial and temporal memory system.
2. Description of the Related Arts
Predictive analytics refers to a variety of techniques for modeling and data mining current and past data sets to make predictions. Predictive analytics allows for the generation of predictive models by identifying patterns in the data sets. Generally, the predictive models establish relationships or correlations between various data fields in the data sets. Using the predictive models, a user can predict the outcome or characteristics of a transaction or event based on available data. For example, predictive models for credit scoring in financial services factor in a customer's credit history and data to predict the likeliness that the customer will default on a loan.
Commercially available products for predictive analytics include products from IBM SSPS, KXEN, FICO, TIBCO, Portrait, Angoss, and Predixion Software, just to name a few. These software products use one or more statistical techniques such as regression models, discrete choice models, time series models and other machine learning techniques to generate useful predictive models. These software products generate different predictive models having different accuracies and characteristics depending on, among others, the amount of training data and available resources.
Each of these software products has different capabilities and requirements. Most of these software products involve an extensive amount of user configuration to product predictive models that is suitable for use. Such user configuration involves much time and experience on the part of users. In order to implement some advanced features, various complicated user operations and configurations are typically needed.